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Titlebook: Bayesian Inference with Geodetic Applications; Karl-Rudolf Koch Book 1990 Springer-Verlag Berlin Heidelberg 1990 Likelihood.Monte Carlo in

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發(fā)表于 2025-3-21 16:21:35 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Bayesian Inference with Geodetic Applications
影響因子2023Karl-Rudolf Koch
視頻videohttp://file.papertrans.cn/182/181854/181854.mp4
學科分類Lecture Notes in Earth Sciences
圖書封面Titlebook: Bayesian Inference with Geodetic Applications;  Karl-Rudolf Koch Book 1990 Springer-Verlag Berlin Heidelberg 1990 Likelihood.Monte Carlo in
影響因子This introduction to Bayesian inference places special emphasis on applications. All basic concepts are presented: Bayes‘ theorem, prior density functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, mixed model and in the model with unknown variance and covariance components is considered. Solutions are supplied for the classification, for the posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.
Pindex Book 1990
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0930-0317 r the posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.978-3-540-53080-0978-3-540-46601-7Series ISSN 0930-0317 Series E-ISSN 1613-2580
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Book 1990ions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, m
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0930-0317 sity functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinea
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